To be or not to be: Control and balancing of type I and type II errors

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Abstract

The various costs of Type I and Type II errors of inference from data are discussed. Unfortunately, other things equal, Types I and II errors are inversely related. Six methods of minimizing Type I error in studies are presented, each of which may be employed even after data are collected. Type II errors may also be minimized by a combination of study design and analytic means. Although some of these techniques must be built into the study others can be undertaken when beginning work on a data set. Only one of these techniques will increase Type I error, and those involving data reduction may decrease both types of error.

References (1)

  • J. Cohen

    The cost of dichotimization

    (1982)

Cited by (0)

I am indebted to Jacob Cohen and Elmer Struening for their very helpful comments on an earlier draft of this paper, and to Detra Allen for her assistance in organizing and typing the manuscript.

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